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                <h1 id="Tensor基本操作"><a href="#Tensor基本操作" class="headerlink" title="Tensor基本操作"></a>Tensor基本操作</h1><h2 id="Tensor属性"><a href="#Tensor属性" class="headerlink" title="Tensor属性"></a>Tensor属性</h2><ol>
<li><p>torch.dtype 是展示 torch.Tensor 数据类型的类<br>FloatTensor、DoubleTensor、HalfTensor、ByteTensor、CharTensor、ShortTensor、IntTensor、LongTensor</p>
</li>
<li><p>Torch.device 是表现 torch.Tensor 被分配的设备类型的类<br>查看设备</p>
<pre><code>torch.device(&#39;cpu&#39;)
torch.device(&#39;cuda&#39;)
torch.device(&#39;cpu:0&#39;)
torch.device(&#39;cuda:0&#39;)
torch.device(&#39;cpu&#39;, 0)
torch.device(&#39;cuda&#39;, 0)
</code></pre>
<p>声明设备</p>
<pre><code>device_cpu = torch.device(&quot;cpu:0&quot;)
device_cuda = torch.device(&quot;cuda:0&quot;)
</code></pre>
<p>挂载设备</p>
<pre><code>data = torch.Tensor([1])
data.to(device_cpu)
data.to(device_cuda)
</code></pre>
</li>
<li><p>torch.layout 是表现 torch.Tensor 内存分布的类(目前只支持 torch.strided)</p>
</li>
</ol>
<h2 id="Tensor创建"><a href="#Tensor创建" class="headerlink" title="Tensor创建"></a>Tensor创建</h2><ol>
<li>直接创建<br>torch.tensor(data, dtype=None, device=None,requires_grad=False)<br>data - 可以是list, tuple, numpy array, scalar或其他类型<br>dtype - 可以返回想要的tensor类型<br>device - 可以指定返回的设备<br>requires_grad - 可以指定是否进行记录图的操作，默认为False<br>注：torch.tensor 总是会复制 data, 如果你想避免复制，可以使 torch.Tensor. detach()；如果是从 numpy 中获得数据，那么你可以用 torch.from_numpy(), 注from_numpy() 是共享内存的。<br>例：<br>完整的写法</li>
</ol>
<pre><code>torch.tensor([[0.11111, 0.222222, 0.3333333]],
                dtype=torch.float64,
                device=torch.device(&#39;cuda:0&#39;))
</code></pre>
<p> 创建空的tensor</p>
<pre><code>torch.tensor([])
</code></pre>
<ol start="2">
<li>numpy转化tensor<br>torch.from_numpy(ndarry)<br>注：生成返回的tensor会和ndarry共享数据，任何对tensor的操作都会影响到ndarry,反之亦然<br>例：</li>
</ol>
<pre><code>&gt;&gt;&gt; a = numpy.array([1, 2, 3])
&gt;&gt;&gt; t = torch.from_numpy(a)
&gt;&gt;&gt; t
tensor([ 1,  2,  3])
&gt;&gt;&gt; t[0] = -1
&gt;&gt;&gt; a
array([-1,  2,  3])
</code></pre>
<ol start="3">
<li><p>定制Tensor</p>
<ol>
<li>根据数值要求</li>
</ol>
<pre><code>torch.zeros(*sizes, out=None, ..) # 返回大小为sizes的零矩阵 
torch.zeros_like(input, ..) # 返回与input相同size的零矩阵
torch.ones(*sizes, out=None, ..) # f返回大小为sizes的单位矩阵
torch.ones_like(input, ..) # 返回与input相同size的单位矩阵
torch.full(size, fill_value, …) # 返回大小为sizes,单位值为fill_value的矩阵
torch.full_like(input, fill_value, …) # 返回与input相同size，单位值为fill_value的矩阵
torch.arange(start=0, end, step=1, …) # 返回从start到end, 单位步长为step的1-d tensor.
torch.linspace(start, end, steps=100, …)  # 返回从start到end, 间隔中的插值数目为steps的1-d tensor
torch.logspace(start, end, steps=100, …) # 返回1-d tensor ，从10^start到10^end的steps个对数间隔
</code></pre>
<ol start="2">
<li>根据矩阵要求</li>
</ol>
<pre><code>torch.eye(n, m=None, out=None,…) # 返回2-D 的单位对角矩阵
torch.empty(*sizes, out=None, …) # 返回被未初始化的数值填充，大小为sizes的tensor
torch.empty_like(input, …) # 返回与input相同size,并被未初始化的数值填充的tensor
</code></pre>
<ol start="3">
<li>根据随机生成</li>
</ol>
<pre><code>torch.normal(mean, std, out=None)
torch.rand(*size, out=None, dtype=None, …) #返回[0,1]之间均匀分布的随机数值
torch.rand_like(input, dtype=None, …) #返回与input相同size的tensor, 填充均匀分布的随机数值
torch.randint(low=0, high, size,…) #返回均匀分布的[low,high]之间的整数随机值
torch.randint_like(input, low=0, high, dtype=None, …) #
torch.randn(*sizes, out=None, …) #返回大小为size,由均值为0，方差为1的正态分布的随机数值
torch.randn_like(input, dtype=None, …)
torch.randperm(n, out=None, dtype=torch.int64) # 返回0到n-1的数列的随机排列
</code></pre>
</li>
</ol>
<h2 id="Tensor操作"><a href="#Tensor操作" class="headerlink" title="Tensor操作"></a>Tensor操作</h2><ol>
<li><p>基本操作：</p>
<ol>
<li>合并操作</li>
</ol>
<pre><code>torch.cat(seq, dim=0, out=None) # 沿着dim连接seq中的tensor, 所有的tensor必须有相同的size或为empty
torch.stack(seq, dim=0, out=None) # 同上
</code></pre>
<p>注：<br>.cat 和 .stack的区别在于 cat会增加现有维度的值,可以理解为续接，stack会新加增加一个维度，可以<br>理解为叠加<br>例：</p>
<pre><code>&gt;&gt;&gt; a=torch.Tensor([1,2,3])
&gt;&gt;&gt; torch.stack((a,a)).size()
torch.size(2,3)
&gt;&gt;&gt; torch.cat((a,a)).size()
torch.size(6)
</code></pre>
<ol start="2">
<li>剪裁操作</li>
</ol>
<pre><code>torch.split(tensor, split_size_or_sections, dim=0) # 将tensor 拆分成相应的组块
torch.chunk(tensor, chunks, dim=0) # 将tensor 拆分成相应的组块，最后一块会小一些如果不能整除的话
</code></pre>
<p>注：<br>split和chunk的区别在于：split的split_size_or_sections 表示每一个组块中的数据大小，chunks表示组块的数量<br>例：</p>
<pre><code>&gt;&gt;&gt; a = torch.Tensor([1,2,3])
&gt;&gt;&gt; torch.split(a,1)
(tensor([1.]), tensor([2.]), tensor([3.]))
&gt;&gt;&gt; torch.chunk(a,1)
(tensor([ 1., 2., 3.]),)
</code></pre>
<ol start="3">
<li>索引操作</li>
</ol>
<pre><code>torch.index_select(input, dim, index, out=None) 
# 返回沿着dim的指定tensor, index需为longTensor类型，不共用内存
torch.masked_select(input, mask, out=None) 
# 根据mask来返回input的值其为1-D tensor. Mask为ByteTensor, true返回，false不返回，返回值不共用内存
</code></pre>
<p>例:</p>
<pre><code>&gt;&gt;&gt; x = torch.randn(3, 4)
&gt;&gt;&gt; x
tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],
     [-1.2035,  1.2252,  0.5002,  0.6248],
     [ 0.1307, -2.0608,  0.1244,  2.0139]])
&gt;&gt;&gt; mask = x.ge(0.5)
&gt;&gt;&gt; mask
tensor([[ 0,  0,  0,  0],
     [ 0,  1,  1,  1],
     [ 0,  0,  0,  1]], dtype=torch.uint8)
&gt;&gt;&gt; torch.masked_select(x, mask)
tensor([ 1.2252,  0.5002,  0.6248,  2.0139])
</code></pre>
<ol start="4">
<li>变换操作</li>
</ol>
<pre><code>torch.transpose(input, dim0, dim1, out=None) # 返回dim0和dim1交换后的tensor
torch.t(input, out=None) # 专为2D矩阵的转置，是transpose的便捷函数
torch.squeeze(input, dim, out=None)  # 默认移除所有size为1的维度，当dim指定时，
移除指定size为1的维度. 返回的tensor会和input共享存储空间，所以任何一个的改变都会影响另一个
torch.unsqueeze(input, dim, out=None) # 扩展input的size, 如 A x B 变为 1 x A x B 
torch.reshape(input, shape) # 返回size为shape具有相同数值的tensor, 注意 shape=(-1,)这种表述，-1表示任意的。
torch.where(condition, x, y) # 根据condition的值来相应x,y的值，true返回x的值，false返回y的值，形成新的tensor
torch.unbind(tensor, dim=0) # 返回tuple 解除指定的dim的绑定,相当于按指定dim拆分
torch.nonzero(input, out=None) # 返回非零值的索引， 每一行都是一个非零值的索引值
</code></pre>
<p>例：</p>
<pre><code>&gt;&gt;&gt; a=torch.Tensor([1,2,3,4,5]) #a.size 是 torch.size(5)
&gt;&gt;&gt; b=a.reshape(1,-1)  #表示第一维度是1，第二维度按a的size填充满
&gt;&gt;&gt; b.size()
torch.size([1,5])

&gt;&gt;&gt; a=torch.Tensor([[1,2,3],[2,3,4]])
&gt;&gt;&gt; torch.unbind(a,dim=0)
(torch([1,2,3]),torch([2,3,4])) # 将一个(2,3) 分为两个(3)

torch.nonzero(input, out=None) # 返回非零值的索引， 每一行都是一个非零值的索引值

&gt;&gt;&gt; torch.nonzero(torch.tensor([1, 1, 1, 0, 1]))
tensor([[ 0],
        [ 1],
        [ 2],
        [ 4]])
&gt;&gt;&gt; torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
                                [0.0, 0.4, 0.0, 0.0],
                                [0.0, 0.0, 1.2, 0.0],
                                [0.0, 0.0, 0.0,-0.4]]))
tensor([[ 0,  0],
        [ 1,  1],
        [ 2,  2],
        [ 3,  3]])
</code></pre>
</li>
<li><p>点对点操作</p>
<ol>
<li>三角函数</li>
</ol>
<pre><code>torch.abs(input, out=None)
torch.acos(input, out=None)
torch.asin(input, out=None)
torch.atan(input, out=None)
torch.atan2(input, inpu2, out=None) 
torch.cos(input, out=None)
torch.cosh(input, out=None)
torch.sin(input, out=None)
torch.sinh(input, out=None)
torch.tan(input, out=None)
torch.tanh(input, out=None)
</code></pre>
<ol start="2">
<li>加减乘除</li>
</ol>
<pre><code>Torch.add(input, value, out=None)
     .add(input, value=1, other, out=None)
     .addcdiv(tensor, value=1, tensor1, tensor2, out=None)
     .addcmul(tensor, value=1, tensor1, tensor2, out=None)
torch.div(input, value, out=None)
     .div(input, other, out=None)
torch.mul(input, value, out=None)
     .mul(input, other, out=None)
</code></pre>
<ol start="3">
<li>对数运算</li>
</ol>
<pre><code>torch.log(input, out=None)  # y_i=log_e(x_i)
torch.log1p(input, out=None)  # y_i=log_e(x_i+1)
torch.log2(input, out=None)   # y_i=log_2(x_i)
torch.log10(input,out=None)  # y_i=log_10(x_i)
</code></pre>
<ol start="4">
<li>幂函数</li>
</ol>
<pre><code>torch.pow(input, exponent, out=None)  # y_i=input^(exponent)
</code></pre>
<ol start="5">
<li>指数函数</li>
</ol>
<pre><code>torch.exp(tensor, out=None)    # y_i=e^(x_i)
torch.expm1(tensor, out=None)   # y_i=e^(x_i) -1
</code></pre>
<ol start="6">
<li>截断函数</li>
</ol>
<pre><code>torch.ceil(input, out=None)   # 返回向正方向取得最小整数
torch.floor(input, out=None)  # 返回向负方向取得最大整数
torch.round(input, out=None)  # 返回相邻最近的整数，四舍五入
torch.trunc(input, out=None)  # 返回整数部分数值
torch.frac(tensor, out=None)  # 返回小数部分数值
torch.fmod(input, divisor, out=None)  # 返回input/divisor的余数
torch.remainder(input, divisor, out=None)  # 同上
</code></pre>
<ol start="7">
<li>其他运算</li>
</ol>
<pre><code>torch.erf(tensor， out=None)
torch.erfinv(tensor, out=None)
torch.sigmoid(input, out=None)
torch.clamp(input, min, max out=None)  # 返回 input&lt;min,则返回min, input&gt;max,则返回max,其余返回input
torch.neg(input, out=None) # out_i=-1*(input)
torch.reciprocal(input, out=None)  # out_i= 1/input_i
torch.sqrt(input, out=None)  # out_i=sqrt(input_i)
torch.rsqrt(input, out=None) # out_i=1/(sqrt(input_i))
torch.sign(input, out=None)  # out_i=sin(input_i)  大于0为1，小于0为-1
torch.lerp(start, end, weight, out=None)
</code></pre>
</li>
<li><p>降维操作</p>
</li>
</ol>
<pre><code>torch.argmax(input, dim=None, keepdim=False) # 返回最大值排序的索引值
torch.argmin(input, dim=None, keepdim=False)  # 返回最小值排序的索引值
torch.cumprod(input, dim, out=None)  # y_i=x_1 * x_2 * x_3 *…* x_i
torch.cumsum(input, dim, out=None)  # y_i=x_1 + x_2 + … + x_i
torch.dist(input, out, p=2)       # 返回input和out的p式距离
torch.mean()                      # 返回平均值
torch.sum()                       # 返回总和
torch.median(input)               # 返回中间值
torch.mode(input)                 # 返回众数值
torch.unique(input, sorted=False) # 返回1-D的唯一的tensor,每个数值返回一次.
torch.std() # 返回标准差
torch.var() # 返回方差
torch.norm(input, p=2) # 返回p-norm的范式
torch.prod(input, dim, keepdim=False) # 返回指定维度每一行的乘积
</code></pre>
<ol start="4">
<li>对比操作</li>
</ol>
<pre><code>torch.eq(input, other, out=None)  # 按成员进行等式操作，相同返回1
torch.equal(tensor1, tensor2) # 如果tensor1和tensor2有相同的size和elements，则为true
torch.ge(input, other, out=None)   # input&gt;= other
torch.gt(input, other, out=None)   # input&gt;other
torch.le(input, other, out=None)    # input=&lt;other
torch.lt(input, other, out=None)    # input&lt;other
torch.ne(input, other, out=None)  # input != other 不等于
torch.max()                        # 返回最大值
torch.min()                        # 返回最小值
torch.isnan(tensor) # 判断是否为’nan’
torch.sort(input, dim=None, descending=False, out=None) # 对目标input进行排序
torch.topk(input, k, dim=None, largest=True, sorted=True, out=None)  # 沿着指定维度返回最大k个数值及其索引值
torch.kthvalue(input, k, dim=None, deepdim=False, out=None) # 沿着指定维度返回最小k个数值及其索引值
</code></pre>
<ol start="5">
<li>频谱操作</li>
</ol>
<pre><code>torch.fft(input, signal_ndim, normalized=False)
torch.ifft(input, signal_ndim, normalized=False)
torch.rfft(input, signal_ndim, normalized=False, onesided=True)
torch.irfft(input, signal_ndim, normalized=False, onesided=True)
torch.stft(signa, frame_length, hop, …)
</code></pre>
<ol start="6">
<li>其他操作</li>
</ol>
<pre><code>torch.cross(input, other, dim=-1, out=None)  # 叉乘(外积)
torch.dot(tensor1, tensor2)  # 返回tensor1和tensor2的点乘
torch.mm(mat1, mat2, out=None) # 返回矩阵mat1和mat2的乘积
torch.eig(a, eigenvectors=False, out=None) # 返回矩阵a的特征值/特征向量 
torch.det(A)  # 返回矩阵A的行列式
torch.trace(input) # 返回2-d 矩阵的迹(对对角元素求和)
torch.diag(input, diagonal=0, out=None) #
torch.histc(input, bins=100, min=0, max=0, out=None) # 计算input的直方图
torch.tril(input, diagonal=0, out=None)  # 返回矩阵的下三角矩阵，其他为0
torch.triu(input, diagonal=0, out=None) # 返回矩阵的上三角矩阵，其他为0
</code></pre>
<h2 id="Tips"><a href="#Tips" class="headerlink" title="Tips"></a>Tips</h2><ol>
<li>获取python number</li>
</ol>
<pre><code>&gt;&gt;&gt; a = torch.Tensor([1,2,3])
&gt;&gt;&gt; a[0]   # 直接取索引返回的是tensor数据
tensor(1.)
&gt;&gt;&gt; a[0].item()  # 获取python number
1
</code></pre>
<ol start="2">
<li>tensor设置</li>
</ol>
<pre><code>torch.is_tensor()  # 如果是pytorch的tensor类型返回true
torch.is_storage() # 如果是pytorch的storage类型返回ture
</code></pre>
<ol start="3">
<li>tensor是否为空</li>
</ol>
<pre><code>&gt;&gt;&gt; a=torch.Tensor()
&gt;&gt;&gt; len(a)
0
&gt;&gt;&gt; len(a) is 0
True
</code></pre>
<h2 id="后记"><a href="#后记" class="headerlink" title="后记"></a>后记</h2><p>引自：<a target="_blank" rel="noopener external nofollow noreferrer" href="https://www.zhihu.com/people/yan-qiu-65">https://www.zhihu.com/people/yan-qiu-65</a><br>如有侵权，请联系删除。</p>

                
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